# Quick start

by: Kevin Broløs & Tom Jelen

(Feyn version 3.0 or newer)

## A Feyn tour around the block

Welcome to this quick guided tour. We'll get you set up and taking advantage of a `QLattice`

in no time.

## Installation

`Feyn`

is available as Python3.8+ package through `pip`

. You can install it with the following command:

```
richard@feyn:~$ pip3 install feyn
```

Once installed, go to your preferred `Python`

environment and follow along with this example.

## Instantiate a QLattice

If you're using a community `QLattice`

then get started with it by:

```
import feyn
ql = feyn.QLattice()
```

If you have paid for a license to use the `QLattice`

then here's a guide on how to set it up instead of using the community version.

## Auto run

The quickest way to get started is to use the `auto_run`

function on the `QLattice`

. Below you can see how to use `auto_run`

function in a regression or a classification problem.

### Regression

We can make a regression problem using `feyn.datasets.make_regression`

. Then we use the `auto_run`

function to find models for the dataset.

`from feyn.datasets import make_regression`

train, test = make_regression()

models = ql.auto_run(train, output_name = 'y')

This returns a list of fitted models that are the best the `QLattice`

has sampled, sorted by ascending loss.

### Evaluate

The model with the lowest loss is `models[0]`

. We can evaluate that model with the `plot`

function and with `plot_regression`

.

`best = models[0]`

best.plot(train, test)

best.plot_regression(test)

### Classification

We can make a classification problem using `feyn.datasets.make_classification`

. Then we use the `auto_run`

function to find models for the dataset. We use the `kind`

parameter to tell the `auto_run`

function we want classifier models.

`from feyn.datasets import make_classification`

train, test = make_classification()

models = ql.auto_run(train, output_name = 'y', kind = 'classification')

This returns a list of fitted models that are the best the `QLattice`

has sampled, sorted by ascending loss.

### Evaluate

The model with the lowest loss is `models[0]`

. We can evaluate that model with the `plot`

function and it's ROC curve.

`best = models[0]`

best.plot(train, test)

best.plot_roc_curve(test)

Of course we've got way more in store for you, so take a dive off the deep end of the pool with the rest of our documentation.